mirror of
https://github.com/coleam00/Archon.git
synced 2025-12-24 02:39:17 -05:00
e9226efae5
* updated code to use locally hosted ollama llm, nomic-embed-text model * Updating documentation and creating issue templates. * Small updates to the issue templates * set the embedding model in an environment variable * Updated V3-MCP-SUPPORT code to use local LLMs. * Updated V3-MCP-SUPPORT code to use local LLMs. * Some updates to local embedding models and fixing a couple small issues * Updating root of repo for v3 release branch --------- Co-authored-by: Aniket1995 <abhanavase@gmail.com>
128 lines
3.8 KiB
Python
128 lines
3.8 KiB
Python
from __future__ import annotations
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from typing import Literal, TypedDict
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from langgraph.types import Command
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from openai import AsyncOpenAI
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from supabase import Client
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import streamlit as st
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import logfire
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import asyncio
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import json
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import uuid
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import os
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import sys
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# Import all the message part classes
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from pydantic_ai.messages import (
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ModelMessage,
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ModelRequest,
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ModelResponse,
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SystemPromptPart,
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UserPromptPart,
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TextPart,
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ToolCallPart,
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ToolReturnPart,
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RetryPromptPart,
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ModelMessagesTypeAdapter
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)
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# Add the current directory to Python path
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sys.path.append(os.path.dirname(os.path.abspath(__file__)))
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from archon.archon_graph import agentic_flow
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# Load environment variables
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from dotenv import load_dotenv
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load_dotenv()
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openai_client=None
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base_url = os.getenv('BASE_URL', 'https://api.openai.com/v1')
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api_key = os.getenv('LLM_API_KEY', 'no-llm-api-key-provided')
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is_ollama = "localhost" in base_url.lower()
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if is_ollama:
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openai_client = AsyncOpenAI(base_url=base_url,api_key=api_key)
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else:
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openai_client = AsyncOpenAI(api_key=os.getenv("OPENAI_API_KEY"))
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supabase: Client = Client(
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os.getenv("SUPABASE_URL"),
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os.getenv("SUPABASE_SERVICE_KEY")
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)
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# Configure logfire to suppress warnings (optional)
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logfire.configure(send_to_logfire='never')
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@st.cache_resource
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def get_thread_id():
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return str(uuid.uuid4())
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thread_id = get_thread_id()
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async def run_agent_with_streaming(user_input: str):
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"""
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Run the agent with streaming text for the user_input prompt,
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while maintaining the entire conversation in `st.session_state.messages`.
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"""
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config = {
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"configurable": {
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"thread_id": thread_id
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}
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}
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# First message from user
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if len(st.session_state.messages) == 1:
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async for msg in agentic_flow.astream(
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{"latest_user_message": user_input}, config, stream_mode="custom"
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):
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yield msg
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# Continue the conversation
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else:
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async for msg in agentic_flow.astream(
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Command(resume=user_input), config, stream_mode="custom"
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):
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yield msg
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async def main():
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st.title("Archon - Agent Builder")
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st.write("Describe to me an AI agent you want to build and I'll code it for you with Pydantic AI.")
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st.write("Example: Build me an AI agent that can search the web with the Brave API.")
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# Initialize chat history in session state if not present
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if "messages" not in st.session_state:
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st.session_state.messages = []
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# Display chat messages from history on app rerun
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for message in st.session_state.messages:
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message_type = message["type"]
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if message_type in ["human", "ai", "system"]:
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with st.chat_message(message_type):
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st.markdown(message["content"])
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# Chat input for the user
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user_input = st.chat_input("What do you want to build today?")
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if user_input:
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# We append a new request to the conversation explicitly
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st.session_state.messages.append({"type": "human", "content": user_input})
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# Display user prompt in the UI
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with st.chat_message("user"):
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st.markdown(user_input)
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# Display assistant response in chat message container
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response_content = ""
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with st.chat_message("assistant"):
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message_placeholder = st.empty() # Placeholder for updating the message
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# Run the async generator to fetch responses
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async for chunk in run_agent_with_streaming(user_input):
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response_content += chunk
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# Update the placeholder with the current response content
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message_placeholder.markdown(response_content)
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st.session_state.messages.append({"type": "ai", "content": response_content})
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if __name__ == "__main__":
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asyncio.run(main())
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